Pulse oximetry system

ABSTRACT

In one aspect, a computer-implemented method includes receiving signals corresponding to wavelengths of light detected by an optical sensor placed in proximity to a patient&#39;s body, and for each received signal: separating the signal into an AC signal and a DC signal; separating the AC signal into component signals; analyzing the component signals through a fractional phase transformation to identify a desired component signal and harmonic signals associated with the desired component signal; smoothing the desired component signal, the harmonic signals, and the DC signal; and combining the smoothed desired component signal, the smoothed harmonic signals, and the smoothed DC signal to generate a modulation signal. A modulation ratio signal is generated based on the modulation signals derived from the signals, and a peripheral oxygen saturation (SpO2) of the patient&#39;s body is determined based on the modulation ratio signal.

BACKGROUND

This disclosure relates to pulse oximetry.

Pulse oximetry is used to estimate the time-local average percent oxygensaturation present in the blood of a patient's periphery (SpO₂) basedupon photoplethysmogram (PPG) signals received from a sensor. The basicprinciple of estimating SpO₂ via PPG relies upon the relationshipbetween the relative attenuation of photonic transmission through and/orreflection from the blood volume in tissue at different wavelengths oflight, in response to levels of oxygenation in the blood. Typically, twowavelengths of light are used, e.g., wavelengths in the red and infrared(IR) portions of the spectrum. For each wavelength, PPG is obtained at alocation on the patient's periphery, e.g., a fingertip or an earlobehere forward referred to as the red PPG and the IR PPG respectively andwithout loss of generality. Light sources and detectors would typicallybe implemented using readily-available light-emitting diodes (LEDs) andphotodiodes, respectively. A ratiometric measure is then derived fromparameters extracted from these two PPGs, which due to the relativeattenuation characteristic of the two wavelengths, leads to a generallywell-behaved, monotonic mapping to SpO₂ that is valid for patients overa broad population.

The PPG signal is primarily characterized by two components: a DCcomponent corresponding to the bulk light transmission in the tissue,and an AC component corresponding to the modulation of transmission dueto blood flow (and the change in blood volume due to that flow) uponeach cardiac pulse. The raw modulation of transmission is measured bydetecting the raw amplitude of the AC component of the PPG signal.Normalization of this raw amplitude measure with a measure of the DCcomponent then produces a percentage-modulation term.

Taking the percentage-modulation term from the red PPG signal anddividing it by that of the IR PPG signal forms a normalized ratio R_(N)that is mapped to SpO₂ values. The mapping of R_(N) to SpO₂ values maybe determined through a calibration procedure that is typicallyperformed in a clinical setting (e.g., a hypoxia laboratory), wherevalues of R_(N) are collected simultaneously with reference measures ofarterial oxygen saturation (SaO₂). A set of (R_(N), SaO₂) value pairs iscollected over a set of human subjects to each of whom a controlled setof hypoxia states is induced (while being supervised for safety), thusestablishing a range of corresponding SaO₂ values. A mapping is thendeveloped over the range of (R_(N), SaO₂) value pairs, e.g., using alookup table or a polynomial fit. Applying this map to values of R_(N)then produces corresponding values of SpO₂, taken as an estimate ofSaO₂. Typically, values of SpO₂ are resolvable with accuracy acceptablein the field over a range of roughly 70% to 100%.

The AC component of the PPG signal is comprised of pulses correlated tothe vascular blood flow, with peaks and cyclic behavior according to thecardiac cycle. Because of this, the pulse rate (PR) may be deriveddirectly from this component. Many methods are available, from simplepeak detection and interval counting to sophisticated rate detectionmethods. Often smoothing is applied to compute a time-local average ofthe pulse rate.

A working realization of the SpO₂ measure based only upon the abovebasic principles may be made in a fairly straightforward manner, forexample, using classic filtering and energy detection methods to performthe extraction of AC and DC components and measuring of the ACamplitude. Such a basic method will generally work as long as conditionsare favorable, e.g., the signal to noise ratio (SNR) is relatively high(more specifically, when the percentage-modulation is relatively largecompared to background noise or interference sources).

In practice, challenges can arise due to various conditions that reduceSNR. For example, the patient's own respiration will modulate the PPG,producing a separate cyclical interference component within the ACcomponent signal, sometimes with more raw power than that of the desiredpulse-mediated modulation. Also, motion at the extremity associated withthe optical sensing point can have a similar modulating effect on thePPG, producing either a cyclical or transient interference termdepending upon the motion. In addition, patients can exhibit lowperfusion, which lowers the percentage-modulation and hence theamplitude of the AC signal relative to background noise and/orinterference terms. Such conditions of lowered SNR tend to bias thenormalized ratio R_(N) toward unity (which in practice often tends tobias the mapped SpO₂ values toward the vicinity of 85%), which presentsa real performance challenge for maintaining SpO₂ accuracy over thedesired range across a broad patient population.

SUMMARY

This disclosure describes systems and techniques related to pulseoximetry. The system processes PPG signals simultaneously throughparallel channels. For each channel, the system performs inputprocessing of the PPG signal, analytic wavelet bank processing,quarter-phase (QP) domain harmonic detection and tracking, and QP-domainharmonic state vector filtering and amplitude statistics processing.Input processing of the PPG signal includes sample interpolation toincrease the sample rate of the system to support the frequencyresolution needed for the harmonic processing, filtering to reduceeffects of out-of-band artifacts and to generally improve SNR, and bandseparation of the PPG signal into basic AC and DC components. Analyticwavelet bank processing separates the AC components into n_(B) componentbands each with substantially increased SNR. QP-domain harmonicdetection and tracking includes QP transformation and state vectorgeneration, periodicity detection for estimation of per-QP pulseinterval based upon harmonic pattern identification in QP state space,and tracking of harmonic parameters in QP space for n_(H) harmonics.QP-domain harmonic state vector filtering and amplitude statisticsprocessing includes individual per-harmonic filtering of parameters(QP-space linear smoothing) to form robust moving measures of harmonicamplitude, QP-synchronized filtering of DC components of the PPG signal,combination of harmonic amplitude to form robust measure of the ACcomponent pulse amplitude, combination of the AC information with the DCinformation to form robust moving measures of the normalizedpercent-fraction PPG pulse modulation and moving measure of thenormalized modulation ratio, and QP-synchronized filtering to form theinstantaneous pulse interval. The system then performs statisticalfiltering of the moving measure of the normalized modulation ratio toform a smoothed moving estimate of the normalized modulation ratio andstatistical filtering of the instantaneous pulse interval to form asmoothed moving estimate of instantaneous pulse interval. The systemthen maps the smoothed moving estimate of the normalized modulationratio to output SpO₂ and maps the smoothed moving estimate ofinstantaneous pulse interval to output pulse rate.

The system can provide accurate SpO₂ estimates for smallpercentage-modulation amplitude levels caused by a) relatively lowperfusion (low local blood volume) and b) relatively dark skin (highamount of attenuation of raw signal) by using harmonic QP componentsthat have individually high SNR and averaging tracked harmonic QPparameters in QP time to further increase SNR. The system can beresistant to cardio-pulmonary coupling artifact and motion artifact byusing periodicity pattern detection to track the periodic component dueto pulse and to reject components due to breath artifact and motionartifact.

In general, in one innovative aspect, a system includes an opticalsensor configured to detect light, a display apparatus, one or more dataprocessing apparatus coupled with the optical sensor and the displayapparatus, and a non-transitory computer readable storage medium encodedwith a computer program. The program includes instructions that whenexecuted by the one or more data processing apparatus cause the one ormore data processing apparatus to perform operations. The operationsinclude receiving a first signal corresponding to a first wavelength oflight detected by the optical sensor placed in proximity to a patient'sbody and receiving a second signal corresponding to a second wavelengthof light detected by the optical sensor. For each received signal of thefirst signal and the second signal, the operations include separatingthe signal into an AC signal and a DC signal; separating the AC signalinto component signals, wherein each of the component signals representsa frequency-limited band; identifying, from the component signals orfrom another source such as a heart rate detector based upon an ECG, anunderlying pulse rate; analyzing the component signals through afractional phase transformation to identify a desired component signaland harmonic signals associated with the desired component signal, allassociated locally in time with the underlying pulse rate; smoothing thedesired component signal, the harmonic signals, and the DC signal; andcombining the smoothed desired component signal, the smoothed harmonicsignals, and the smoothed DC signal to generate a modulation signal. Theoperations include generating a modulation ratio signal based on themodulation signal derived from the first signal and the modulationsignal derived from the second signal; determining peripheral oxygensaturation (SpO2) of the patient's body based on the modulation ratiosignal; and causing the display apparatus to display a valuerepresenting the determined SpO2 of the patient's body.

In another aspect, an apparatus includes a non-transitory computerreadable storage medium encoded with a computer program. The programincludes instructions that when executed by the one or more dataprocessing apparatus cause the one or more data processing apparatus toperform operations. The operations include receiving a first signalcorresponding to a first wavelength of light detected by an opticalsensor placed in proximity to a patient's body and receiving a secondsignal corresponding to a second wavelength of light detected by theoptical sensor. For each received signal of the first signal and thesecond signal, the operations include separating the signal into an ACsignal and a DC signal; separating the AC signal into component signals,wherein each of the component signals represents a frequency-limitedband; identifying, from the component signals or from another sourcesuch as a heart rate detector based upon an ECG, an underlying pulserate; analyzing the component signals through a fractional phasetransformation to identify a desired component signal and harmonicsignals associated with the desired component signal, all associatedlocally in time with the underlying pulse rate; smoothing the desiredcomponent signal, the harmonic signals, and the DC signal; and combiningthe smoothed desired component signal, the smoothed harmonic signals,and the smoothed DC signal to generate a modulation signal. Theoperations include generating a modulation ratio signal based on themodulation signal derived from the first signal and the modulationsignal derived from the second signal and determining peripheral oxygensaturation (SpO2) of the patient's body based on the modulation ratiosignal.

In another aspect, a method includes receiving a first signalcorresponding to a first wavelength of light detected by an opticalsensor placed in proximity to a patient's body and receiving a secondsignal corresponding to a second wavelength of light detected by theoptical sensor. For each received signal of the first signal and thesecond signal, the operations include separating the signal into an ACsignal and a DC signal; separating the AC signal into component signals,wherein each of the component signals represents a frequency-limitedband; identifying, from the component signals or from another sourcesuch as a heart rate detector based upon an ECG, an underlying pulserate; analyzing the component signals through a fractional phasetransformation to identify a desired component signal and harmonicsignals associated with the desired component signal, all associatedlocally in time with the underlying pulse rate; smoothing the desiredcomponent signal, the harmonic signals, and the DC signal; and combiningthe smoothed desired component signal, the smoothed harmonic signals,and the smoothed DC signal to generate a modulation signal. The methodincludes generating a modulation ratio signal based on the modulationsignal derived from the first signal and the modulation signal derivedfrom the second signal and determining peripheral oxygen saturation(SpO2) of the patient's body based on the modulation ratio signal.

Various other functions and benefits of such a system will be apparentfrom the foregoing detailed description and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing an example of a pulse oximetry system.

FIG. 2 is a block diagram showing an example of an input processing andband separation unit.

FIG. 3 a is a plot showing an impulse response for one band of ananalytic wavelet bank.

FIG. 3 b is a plot showing frequency responses for the real part of theanalytic wavelet bank.

FIG. 4 shows an example of a pulse oximetry system that includes apatient worn sensor in communication with a display device.

FIG. 5 is a flowchart showing examples of operations performed by apulse oximetry system.

FIG. 6 is a block diagram showing an example of a computing system thatmay be used to implement a pulse oximetry system.

FIG. 7 is a block diagram showing an example of a computing system thatdepicts a host system that may be used to implement a pulse oximetrysystem.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing an example of a pulse oximetry system100. The system 100 includes electronic components, e.g., hardwareand/or software that facilitate pulse oximetry. The system 100 estimatesthe time-local average percent oxygen saturation present in the blood ofa patient's periphery (SpO₂) and the time-local average of the patient'spulse rate (PR). In some implementations, portions of the system 100 areincluded in a patient monitoring device that can continuously collectphysiological vital signs data, such as electrocardiogram (ECG), heartrate, respiratory rate, oxygen saturation, blood pressure, temperature,and pulse rate. Examples of patient monitoring devices include chestsensors, such as those described in U.S. Publication No. 2015/0302539,filed Oct. 22, 2015, titled “Patient Care and Health InformationManagement Systems and Methods” and U.S. Publication No. 2016/0228060,filed Feb. 9, 2016, titled “Patient Worn Sensor Assembly,” and othersensor assemblies that connect with peripheral devices, such as thesensor assembly described in U. S. Publication No. 2018/0213859, filedJan. 25, 2018, titled “Sensor Connection Assembly for Integration withGarment,” the disclosures of which are hereby incorporated by referencein their entirety. In some implementations, portions of the system 100are included in a monitoring station that analyzes and displaysphysiological vital signs data using signals received from one or moreperipheral devices, such as a chest sensor, a fingertip probe, anearlobe probe, or a non-invasive blood pressure (NIBP) cuff. Examples ofmonitoring stations include a bedside monitor and a central serverstation which are also described in U.S. Publication No. 2015/0302539.

The system 100 includes an optical sensor 102 that generatesphotoplethysmogram (PPG) signals (denoted as signals I and R)corresponding to two wavelengths of light, e.g., the infrared and redportions of the spectrum, sensed by the optical sensor 102 at a locationon the patient's periphery, e.g., a fingertip or an earlobe. The opticalsensor 102 can be, for example, affixed to the patient's fingertip orearlobe. The sensor 102 can include light sources and detectorsimplemented using, for example, light-emitting diodes (LEDs) andphotodiodes, respectively. The sensor 102 can continuously acquire PPGto facilitate continuous oxygen saturation and pulse rate monitoring.The sensor 102 can sample PPG at a rate of 74 Hz to 76 Hz so that 50 Hzand 60 Hz illumination interference can be filtered out.

The system 100 processes the I and R signals simultaneously throughparallel channels. For each channel, the system 100 includes an inputprocessing and band separation unit 104, an analytic wavelet bank 106, aquarter-phase (QP) domain harmonic detection and tracking unit 108, anda QP-domain harmonic state vector filtering and amplitude statisticsprocessing unit 110. U.S. Pat. No. 7,702,502 filed Feb. 23, 2006, titled“Apparatus for Signal Decomposition, Analysis and Reconstruction,” U.S.Pat. No. 7,706,992 filed Feb. 23, 2006, titled “System and Method forSignal Decomposition, Analysis and Reconstruction,” U.S. Pat. No.8,086,304 filed Nov. 26, 2008, titled “Physiological Signal Processingto Determine a Cardiac Condition,” and U.S. Pat. No. 9,530,425 filedMar. 17, 2014, titled “Method and Apparatus for Signal Decomposition,Analysis, Reconstruction and Tracking” include disclosures of techniquesapplicable to the processing of the I and R signals when taken assignals generally having quasi-periodic components. The entire contentsof U.S. Pat. Nos. 7,702,502, 7,706,992, 8,086,304, and 9,530,425 arehereby incorporated by reference.

For systems having ratiometric measures, the two processing channels mayhave processing dynamics (e.g., filtering, delays, etc.) that are assimilar as possible, so that dynamic changes in the sensing system(e.g., optical sensor 102) will ripple through the processing channelsin a time-aligned and phase-aligned manner. Thus, as the ripple effectsarrive at the ratio point together, they tend to be cancelled out andthereby minimized at the output, providing robustness againsttransients. For this reason, system 100 may maintain symmetry betweenthe parallel channels at every point to ensure synchronization of delaysbetween the I and R channels and between the AC and DC channels.

In some implementations, the input processing and band separation unit104 performs sample interpolation to increase the sample rate of the PPGsignal to support the frequency resolution needed for the subsequentQP-domain harmonic processing, performs filtering to reduce effects ofout-of-band artifacts and to generally improve SNR, and performs bandseparation of the PPG signal into AC and DC components. FIG. 2 is ablock diagram showing an example of the input processing and bandseparation unit 104. The input processing and band separation unit 104reduces out-of-band interference and artifacts in the raw PPG signal bybandpass filtering the AC component and multi-stage lowpass filteringthe DC component.

In some implementations, the input processing and band separation unit104 includes a bandlimited interpolation unit 202. The bandlimitedinterpolation unit 202 includes an interpolation unit 204 and a lowpassfilter 206 to perform bandlimited interpolation of the PPG signal toattenuate high-frequency (HF) interference and to raise the input samplerate by a factor N_(ITP). In some implementations, the interpolationunit is bypassed, so that effectively N_(ITP)=1.

As described in more detail below with respect to the QP-domain harmonicdetection and tracking unit 108, the system 100 performs extraction andtracking of the time-varying harmonic components of thepulsatile-modulation portion of the PPG signal. The highest frequencyf_(HI) to be resolved (and therefore the system passband) is determinedby the product of 1) the number of harmonics n_(H) to be resolved and 2)the highest pulse rate r_(HI) at which the highest harmonic is to beresolved. The lowpass filter 206 with a corner substantially near tothis frequency can attenuate out-of-band high-frequency interferenceterms and increase the system SNR.

At normal pulse rates, e.g., ranging from normal sinus rhythm (NSR) tomild exercise (˜120 beats per minute (BPM)), the bulk of the harmonicpower (˜90%) is substantially contained in the first three harmonics ofthe PPG signal. Above this range, the harmonic power drops steadily withincreasing pulse rate so that above ˜180 BPM the signal is mostlyfirst-harmonic, i.e., the morphology becomes quasi-sinusoidal. As aresult,f _(HI) =n _(H) ×r _(HI)=3×120 BPM=360 BPM=6 Hz.Frequency f_(HI), being the highest signal frequency of interest,nominally defines the highest-frequency wavelet band needed in theanalytic wavelet bank 106 (described below) for extracting harmoniccontent from the PPG signal. As described in the patents incorporated byreference herein relating to techniques applicable to the processing ofthe I and R signals, the analytic wavelet bank 106 typically restrictsthe scaling interval widths to integer numbers of samples to maintainhigh computational efficiency. This restriction can lead to increasinglycoarse frequency resolution at increasingly high wavelet centerfrequencies, i.e., the wavelet band center frequencies are increasinglyquantized, due to the shrinking scales becoming increasingly affected bythe time quantization imposed by the integer-sample scale widths. Addingto the challenge is the desire to maintain sufficient resolution athigher frequencies to maintain separation of each harmonic component tobe resolved.

The nominal input sample rate of F_(si)=75 Hz for the raw PPG signalwould lead to band-center quantization sufficient to produce inter-bandresponse gaps that are large enough to not be able to resolve harmonicamplitudes at the highest pulse rates without ambiguity. To resolve thisissue, in some implementations, the input processing and band separationunit 104 interpolates samples for processing by the analytic waveletbank 106 by resampling the input signal stream (i.e., “up-sampling”).The bandlimited interpolation unit 202 provides bandlimitedinterpolation implemented through a combination of zero-insertion by theinterpolation unit 204 followed by synchronized lowpass filtering bylowpass filter 206 to “fill in” the added zeros. The lowpass filter'scorner frequency can be set at the highest expected signal frequency tofurther provide bandlimiting so as to optimize SNR againsthigh-frequency noise and artifacts. This up-sampling provides forup-sampled rates F_(s) at integer multiples N_(ITP) of the raw inputsample rate F_(si). An up-sampling factor of N_(ITP)=3 may be used toprovide the frequency resolution needed to properly resolve a thirdharmonic at f_(HI)=6 Hz, thus yielding a nominal internal waveletprocessing sample rate of F_(s)=225 Hz.

The interpolation unit 204 may use zero-insertion, which is acomputationally-efficient operation. The lowpass filter 206 is alsoimplemented with high computational efficiency by using ascaled-integral kernel operator as described in U.S. Pat. No. 7,706,992.The interpolation unit 204 up-samples the raw PPG signal x by a factorof N_(ITP) by insertion of (N_(ITP)−1) zero-valued samples in betweeneach successive input sample. This is followed by the synchronizedlowpass filter 206 implemented using a scaled-integral kernel. Asdescribed in U.S. Pat. No. 7,706,992, the frequency response of thisdigital scaled-integral kernel approximately follows a sinc function inmagnitude, having nulls at integer multiples of (F_(s)/w), where w isthe integrator scale parameter in samples and F_(s) is the up-sampledsample rate. Setting w to an integer multiple of N_(ITP) places aresponse null at the new sample rate, thereby eliminating anytime-domain ripple artifacts introduced by the zero-interpolationprocess. The scale is thus set according to the formula:w _(p) =N _(ITP)·round(F _(s)/(N _(ITP) ·f _(HI) /k _(6 dB))),which yields the closest scale in samples (sampled at rate F_(s)) thatis an integer multiple of N_(ITP) to place the −6 dB lowpass cornerfrequency as close as possible to f_(HI), where the factor k_(6 dB) isthe ratio of the −6 dB point to the first frequency-response null of thelowpass filter 206 and depends upon the order of the scaled integratoroperator.

The overall smoothness and out-of-band HF rejection of the finalinterpolated result is then controlled by the size of integer factor(w_(p)/N_(ITP)), i.e., the number of input samples within the scale ofthe integrator, and the order of the scaled integrator. A second-orderintegrator kernel may provide a sufficient degree of smoothness andout-of-band HF rejection. For a nominal input sample rate of F_(si)=75Hz, the following parameters for the bandlimited interpolator scaledintegral may be used:

TABLE 1 N_(ITP) = 3, F_(s) = 225 Hz, f_(HI) = 6 Hz w_(p) N_(i) D_(ITP)18 2 17where k_(6 dB)=0.44295 for N_(i)=2, and D_(ITP) is the intrinsic delayin samples at rate F_(s).

The AC component of the up-sampled and bandlimited PPG signal isseparated through a highpass filter 208. The highpass filter 208 isdesigned to remove the large DC offset associated with the raw opticaltransmission in the PPG signal, giving an output that is centered on azero baseline, and to reject as much low-frequency artifact as ispractical below the lower nominal pulse rate band edge (in someimplementations, 30 BPM or 0.5 Hz, corresponding to a pulse interval of2 seconds beat-to-beat). In some implementations, the highpass filter208 can have a linear-phase response. Given the size of the time scale,the highpass filter 208 can have a minimum-phase response to minimizetime lags in the system due to the filter's intrinsic group delay. Usinga recursive-form IIR filter topology may lead to a memory-efficientfilter architecture. A second-order response can be used to provide goodstability and sufficient rejection in the stop band. The highpass filter208 is specified according to the following analog-domain transferfunction:

${H_{HP}(s)} = \frac{s^{2}}{s^{2} + {\frac{\omega_{c}}{Q}s} + \omega_{c}^{2}}$where ω_(c) controls the corner frequency (in radians/sec) and Qcontrols the resonance of the filter at the corner. The value of Q canbe set to, for example, Q=0.720, to maximize response flatness in thepassband and response sharpness at the corner, while minimizing ringingin the impulse response. For this value of Q, the formulaω_(c)=2πk_(Q)f_(c) sets the −6 dB point of the filter at frequencyf_(c)=0.5 Hz with factor k_(Q)=1.325.

The highpass filter 208 can be implemented digitally using asecond-order difference equation of the form:a ₀ y _(HP)(n)=b ₀ x(n)+b ₁ x(n−1)+b ₂ x(n−2)−a ₁ y _(HP)(n−1)−a ₂ y_(HP)(n−2)where x(n) and y_(HP) (n) are the signal input and output of thehighpass filter 208 at sample time n, respectively. The filter responseis determined by the coefficients a_(i) and b_(j). The highpass filter208 can employ the Bilinear Transform to produce a digital filter thatapproximates the analog model, as it can provide very close matching ofanalog filter responses, particularly in their passband and transitionbands. The resulting digital filter coefficients are as follows:

TABLE 2 a₀ 1.0 a₁ −1.97429463444953 a₂ 0.974632543411999 b₀0.987231794465383 b₁ −1.97446358893077 b₂ 0.987231794465383

The highpass filter 208 additionally provides a complementary lowpassfilter output formed as the path difference between the input and outputof the highpass filter 208, yielding in the sample domain,y_(I_HP)(n)=x(n)−y_(HP)(n). While the lowpass filter formed in thismanner does not generally have particularly steep cutoff characteristicsin its stopband, its group delay tends to match that of the highpassfilter 208 in the transition band, thus working to preserve timealignment along the corresponding processing stages of the AC and DCsignal paths for both the R and I signals.

The output y_(HP) of the highpass filter 208 is tapped to make x_(ac),forming the filtered AC component of the raw PPG signals (both R and I)from which the modulation amplitudes are estimated. As this waveformcontains the pulsatile component of the PPG signal, it may also be usedfor estimation of the pulse rate PR. This signal may itself also be usedto display a clean, zero-centered PPG waveform (e.g., on the display120, such as a bedside monitor or central nursing station as describedin U.S. Publication No. 2015/0302539). Usually, the filtered ACcomponent resulting from the I signal would be used for this purpose asthe I signal is typically larger in amplitude.

The output y_(I_HP) of the complementary lowpass is tapped to makex_(LP), forming a time-aligned, partially-separated DC component, and soserves advantageously as the input to the lowpass filter 210 byproviding some initial lowpass attenuation of AC components abovefc withalmost no additional computational cost.

The dedicated linear-phase lowpass smoothing filter 210 separates the DCcomponent of the up-sampled/bandlimited PPG signal. The lowpass filter210 is designed to remove all residual pulsatile and transientcomponents from the raw PPG signal, so as to provide the moving-average,time-local value representing the raw optical transmission in the PPGsignal. In addition, its time delay is aligned to match that of thecorresponding processing path of the AC component. The lowpass filter210 is implemented using a scaled integrator kernel, resulting in acomputationally-efficient realization.

As shown in FIGS. 1 and 2 , the AC processing path includes theAC-component highpass filter 208, along with the analytic wavelet bank106 and the QP-domain harmonic detection and tracking unit 108, beforeappearing at the input of the QP-domain harmonic state vector filteringand amplitude statistics processing unit 110, where processing of the ACcomponent occurs in combination with the separated DC component.According to the principles described previously regarding minimizingtransients due to underlying signal changes, the intrinsic delays of theAC and DC processing paths to the QP-domain harmonic state vectorfiltering and amplitude statistics processing unit 110 are designed toboth be equal. These delays, respectively D_(ac) and D_(dc) from theinput x of the interpolation unit 204 of FIG. 2 to the QP-domainharmonic state vector filtering and amplitude statistics processing unit110 of FIG. 1 , are as follows:D _(ac) =D _(ITP) +D _(HP) +D _(BNK)D _(dc) =D _(ITP) +D _(HP) +D _(LP)where D_(BNK) is the intrinsic delay of the analytic wavelet bank 106,D_(HP) is the group delay of the highpass filter 208 of the inputprocessing and band separation unit 104, and D_(LP) is the intrinsicdelay of the DC component lowpass filter 210 of the input processing andband separation unit 104. The DC path delay includes D_(HP) because theinput of the DC component lowpass filter 210 is taken from thecomplementary-lowpass output x_(LP) of the highpass filter 208 asdescribed above. The QP-domain harmonic detection and tracking unit 108is a memoryless operation and so has effectively a zero intrinsic delay.Thus, for D_(ac) and D_(dc) to be equal, D_(LP)=D_(BNK). The scale ofthe DC component lowpass filter 210 to satisfy this is determined fromthe delay by the formula:w _(p)=2·D _(LP) /N _(i)+1which is integer-valued as long as 2·D_(LP) is an integer multiple ofthe order N_(i), and is ensured an integer if N_(i)=2 and D_(LP) is alsoan integer. As will be discussed below, in one example, the analyticwavelet bank 106 has intrinsic delay D_(BNK)=1833. For a processingsample rate of F_(s)=225 Hz, the parameters for the lowpass filter 210would be as follows:

TABLE 3 w_(p) N_(i) D_(LP) 1834 2 1833

The −6 dB lowpass cutoff for the lowpass filter 210 isF_(c)=k_(6 dB)·F_(s)/w, or 0.0543 Hz in this example, which can resultin almost complete attenuation of pulsatile components and veryeffective DC smoothing (note that k_(6 dB)=0.44295 for Ni=2). The outputof the lowpass filter 210 is the moving estimate of the raw DCtransmission component of the PPG signals, both R and I, time aligned tothe moving modulation amplitude estimates for purposes of normalizationand subsequent processing.

Referring to FIG. 1 , the analytic wavelet bank 106 processes theresulting AC component of the PPG signal. The analytic wavelet bank 106performs separation of the AC components into n_(B) component bands eachwith substantially increased SNR. As described in U.S. Pat. Nos.7,706,992 and 9,530,425, the analytic wavelet bank 106 separates theinput AC signal into quasi-periodic components, each of which containsinformation that is significantly bandlimited relative to the inputsignal bandwidth. As a result, those bands containing informationassociated with local periodicities contained in the input signal willpresent that information with increased SNR.

The analytic wavelet bank 106 can be designed as a bank of bandpassfilters with adjacently-spaced band centers. Each bandpass filterresponds by varying amounts to periodicities present in the bank inputsignal according to its frequency response, having the greatest outputamplitude for frequencies closest to the center frequency of thebandpass. For a bank input signal containing (quasi) periodiccomponents, the filters substantially having the greatest outputamplitude at a point in time, relative to their neighbors, will be thosefrequency bands with centers closest to the frequencies of those signalcomponents. A tracing of the band amplitudes as a function of bandfrequency at that time point would reveal “peaks” at those associatedfrequencies. Finding and tracking these amplitude peaks as a function oftime, along with the underlying information in the bands indexed bythose peaks, forms the underlying principle for the design of theanalytic wavelet bank 106. Another advantage of using bandpass filtersis that each filter in the bank has relatively narrow bandwidth, thushaving generally higher SNR relative to the associated frequencycomponent.

A design consideration for the analytic wavelet bank 106 is theso-called “time-frequency frame” of the system 100, controlled throughthe “Q” (relative bandwidth) of the bandpass filters. Closely tied tothe time-frequency frame is the frequency spacing of the bands. Excessband overlap decreases the selectivity between adjacent bands, whichtends to “blur” the sharpness of the spectral peak for identifying theassociated component. However, insufficient band overlap can lead to anexcess of amplitude loss in the crossover region between band centers,leaving “holes” in the spectral representation of the signal, and inextreme cases leading to ambiguity in the spectral peak patterns. HigherQ will create a narrower filter bandwidth, allowing filters to be moreclosely spaced in frequency for a given amount of band overlap, thusproviding increased frequency resolution. However, implementing a narrowfilter bandwidth may require having an impulse response that is wider intime, which in turn translates to lower time resolution. Hence, thechoice of Q represents a tradeoff between frequency resolution and timeresolution.

As described in U.S. Pat. No. 9,530,425, the processing in QP spaceallows for time-frequency information to be captured with much higherprecision in both time and frequency than conveyed by simply using thecenter frequency of a given band as the rate estimate. This propertyrelaxes the requirement for frequency resolution among the band centersstrictly to resolve frequency. In theory, a minimum of two bands peroctave would be needed to isolate the fundamental harmonic from thesecond harmonic of the input periodic component, though in practice morebands may be required for low inter-band response crossover loss.

A wavelet support (impulse response length) approximately 3 cycles longmay provide a good combination of both frequency isolation forcomponents and adequately fast response times to resolve expected timedynamics associated with variabilities in pulse rates. An inter-bandcrossover dip level of less than −0.5 dB from the band centers mayprovide sufficient amplitude accuracy in representing the componentamplitudes for pulse oximetry. Accordingly, the band spacing may be setto 9 bands per octave.

The analytic wavelet bank 106 can be implemented using an augmented formof the Parallel-Form Kovtun-Ricci Wavelet Transform with analyticcomponent bands as described in U.S. Pat. No. 7,706,992. The waveletsare composed of a cascade of scaled-derivative kernels andscaled-integral kernels. The scaled-derivative kernel has a z-domaintransfer function:H _(dp)(Z)=[0.5(1−Z ^(−k) ^(p) )]^(N) ^(dp)having scale parameter k_(p) and order parameter N_(dp) for band p.

In addition to the scaled integral and scaled difference kernels, theanalytic wavelet bank 106 can include in the kernel cascade anadditional kernel type referred to here as a scaled comb kernel, havinga z-domain transfer function:

${H_{cp}(z)} = \left\lbrack \frac{1 - z^{{- 2}\; k_{p}c_{p}}}{c_{p}\left( {1 - z^{{- 2}\; k_{p}}} \right)} \right\rbrack^{N_{cp}}$with scale parameters k_(p) and c_(p), and order parameter N_(cp) forband p. The intrinsic delay of this kernel isD_(cp)=N_(cp)·k_(p)·(c_(p)−1). The frequency response of the scaled combkernel for N_(cp)=1 is a Dirichlet function, which closely approximatesa sinc function at its first mainlobe around DC and then repeats thesemainlobes over frequency at an interval controlled by scale parameterk_(p) (specifically, at a frequency spacing of F_(s)/(2 k_(p)) whereF_(s) is the processing sample rate). The value k_(p) here is set bydesign to the same scaling value k_(p) as appears in the expression forthe scaled difference kernel. This centers the first repeated mainlobeof the scaled comb kernel over the first mainlobe of the scaleddifference kernel, defining a new, more selective bandpass functioncentered at the peak of the mainlobe.

The scaled comb kernel features response nulls to either side of eachrepeated mainlobe, spaced away from each center according to scaleparameter c_(p), such that for c_(p)=2 the nulls are halfway between themainlobe centers, and for larger c_(p), the nulls move proportionallycloser to the mainlobe centers. These nulls have the effect of narrowingthe mainlobe bandwidth, effectively increasing the Q of the filter. TheQ is also increased with increasing order N_(cp), so this kernelfeatures two parameters by which Q may be controlled. Performance may befurther enhanced in that the increased Q obtained due to the added sidenulls, even for c_(p)=2, results in less impulse response widening thanwhen obtaining a similar Q via increasing scaled-derivative filter orderalone. On the other hand, increasing the order may reduce the height ofthe sidelobes between the mainlobe images, and so the order N_(cp)becomes a means to control stopband rejection in the regions just beyondthe first passband nulls.

In a broadband sense, the scaled comb kernel acts as a comb filter,i.e., with a lobe around DC and repeating lobes according to scalek_(p). To obtain a pure bandpass response, the first repeat of themainlobe response above DC, which is centered at frequency F_(s)/(2k_(p)), may be isolated. To reject the lobe at DC, the scaled differencekernel is utilized, as it has a primary response null at DC. The scaledintegral kernel is then utilized to reject the mainlobe images above thefirst, as well as to provide overall smoothing of the impulse response.Using these two kernels in the role of stopband rejection tends to notrequire high orders in practice (e.g., order 4 typically can providesufficient out-of-band rejection).

The parameters for programming the bandpass wavelet responses aretherefore k_(p), c_(p), N_(cp), N_(dp), w_(p), and N_(ip). For the pthbandpass, k_(p) controls the center frequency and Q is controlled by thecombination of (primarily) c_(p), and (secondarily) N_(cp) andscaled-derivative order N_(dp). The parameters N_(cp) and N_(dp) controlthe stopband rejection near DC, and scaled-integral scale w_(p) andorder Nip control the stopband rejection above the passband.

As described in U.S. Pat. No. 7,706,992, scale parameter k_(p) of thetransform wavelet controls the frequency of the peak of the fundamentalbandpass lobe, while scale parameter w_(p) determines the frequency ofthe first null of the lowpass. Choosing the nominal value of w_(p)≈2k_(p)/3 places that first null approximately at the peak of the secondbandpass lobe of H_(cp)(z), i.e. the first repeat of the comb, thuseffectively isolating the fundamental bandpass response. Integral-orderparameter N_(ip) controls the degree of stop-band attenuation for thelowpass operation; a value of N_(ip)=4 may provide effective performanceat low computational load.

Derivative-order parameter N_(dp) controls the bandwidth of the bandpassfunction. For a filter bank, it controls the amount of overlap betweenthe band responses. The values of N_(dp) may be chosen so that the sumof the band responses is approximately flat over the power band, so thatthe total power of the complex is represented in a relatively unbiasedfashion with regard to frequency.

This family of bandpass wavelets is computationally efficient andfeatures linear phase response. As described in U.S. Pat. No. 7,706,992,the transform is implemented with time alignment across the bands topreserve timing information of the underlying component as it varies inrate across bands.

In accordance with the above descriptions and derivations, and for thesampling period as specified previously for the PPG signals, thebandpass wavelets of the analytic wavelet bank 106 can be implementedusing the following design parameters:

TABLE 4 F_(Cp) nominal Band (BPM) k_(p) c_(p) N_(cp) N_(dp) w_(p) Nip 127.92 234 2 4 4 157 4 2 29.98 218 2 4 4 145 4 3 32.37 202 2 4 4 134 4 435.14 186 2 4 4 124 4 5 37.99 172 2 4 4 115 4 6 40.86 160 2 4 4 106 4 744.15 148 2 4 4 99 4 8 48.04 136 2 4 4 91 4 9 51.83 126 2 4 4 85 4 1055.42 118 2 4 4 78 4 11 60.52 108 2 4 4 72 4 12 65.33 100 2 4 4 67 4 1369.58 94 2 4 4 62 4 14 75.94 86 2 4 4 58 4 15 81.73 80 2 4 4 53 4 1688.36 74 2 4 4 49 4 17 96.02 68 2 4 4 46 4 18 102.2 64 2 4 4 42 4 19112.6 58 2 4 4 39 4 20 121.0 54 2 4 4 36 4 21 130.5 50 2 4 4 34 4 22142.0 46 2 4 4 31 4 23 148.6 44 2 4 4 29 4 24 163.3 40 2 4 4 27 4 25181.0 36 2 4 4 25 4 26 192.1 34 2 4 4 23 4 27 204.5 32 2 4 4 21 4 28217.9 30 2 4 4 20 4 29 234.0 28 2 4 4 18 4 30 251.7 26 2 4 4 17 4 31272.4 24 2 4 4 16 4 32 298.0 22 2 4 4 14 4 33 327.4 20 2 4 4 13 4 34363.2 18 2 4 4 12 4

The values F_(Cp) are the center frequencies of the bands, defined asthe frequency where the p^(th) band exhibits its peak amplituderesponse. The frequency range covers sufficient range to resolve therequired range of fundamental pulse rates (30 to 240 BPM) as well as theneeded coverage to resolve a third harmonic at up to 120 BPM. Asdescribed above, the bands are spaced at nominally 9 bands per octave,so that full coverage of the desired rate range is achieved with 34bands. The time-domain impulse response resulting for this wavelet orderfeatures an effective time length of approximately 3 cycles, and sorepresents good time resolution for locally-cyclic behavior.

FIG. 3 a is a plot 300 showing the impulse response resulting fromimplementing the above parameters for the first band of the filter bank.Since the wavelets are analytic, the impulse responses arecomplex-valued. The “real” and “imag” traces are the real and imaginaryparts of the impulse response, respectively. The “mag” trace is theinstantaneous complex magnitude at each time point. FIG. 3 b is a plot350 showing frequency responses for the real part of the analyticwavelet bank.

To ensure calibration of the relative responses across the filter bank,the band output signals are normalized to unity gain at the bandcenters. Calibration factors K_(CR) and K_(CI) are determined byevaluating the real-part and imaginary-part amplitude responses(respectively) at their center frequencies F_(Cp) and taking themultiplicative inverse. The real and imaginary filter outputs can bemultiplied by their respective calibration factors, resulting in acalibrated filter bank. Accordingly, the calibration factors for eachcorresponding band are as follows:

TABLE 5 Band K_(CR) K_(CI) 1 2.1062922 2.1089759 2 2.0793090 2.0818756 32.0711782 2.0737073 4 2.0863364 2.0889356 5 2.0953176 2.0979586 62.0671799 2.0696907 7 2.0967443 2.0993922 8 2.0976443 2.1002965 92.1234868 2.1262615 10 2.0603402 2.0628197 11 2.0861533 2.0887516 122.1016212 2.1042917 13 2.0536938 2.0561430 14 2.1223712 2.1251407 152.0668054 2.0693145 16 2.0651836 2.0676854 17 2.1319122 2.1347275 182.0383987 2.0407796 19 2.1123333 2.1150548 20 2.0853243 2.0879190 212.1483483 2.1512437 22 2.1188631 2.1216159 23 2.0502374 2.0526714 242.1235371 2.1263125 25 2.2192235 2.2224760 26 2.1297798 2.1325855 272.0360882 2.0384593 28 2.0828502 2.0854340 29 1.9771225 1.9792380 302.0238779 2.0261950 31 2.0808389 2.0834138 32 1.9476177 1.9496107 332.0038989 2.0061295 34 2.0764990 2.0790548

Referring to FIG. 1 , the QP-domain harmonic detection and tracking unit108 performs QP transformation and state vector generation, robustperiodicity detection for estimation of per-QP pulse interval PI_(QP)based upon harmonic pattern identification in QP state space, andtracking of harmonic parameters in QP space for n_(H) harmonics. Througha QP transform, the detection and tracking unit 108 analyzes thequasi-periodic components of the pulsatile signal to extract criticalamplitude and local phase/frequency parameters occurring at QP updatepoints, producing a time-frequency QP state vector. The QP update pointsare points in time associated with intrinsic information updates in thesignal and occur naturally at rates much lower than the underlyingsample rate of the signal.

The detection and tracking unit 108 detects QP transitions on each ofthe component signals, and forms, at each transition, an associated QPobject as described in U.S. Pat. No. 7,706,992. For purposes of PPGprocessing, the QP objects contain the phase value (signified by a phaselabel), the time value and the component amplitude at the time point ofQP detection, along with the index of the band for the underlyingcomponent. Collecting the current QPs at the current time point into anarray across the bands forms a QP state vector, indexed by band, andupdated upon any QP object update on any band as described in U.S. Pat.No. 7,706,992. The concentrated time-frequency information contained inthis state vector forms the basis to enable the identification andtracking of desired signal components, and the high-precision estimationof their instantaneous frequencies and amplitudes.

The detection and tracking unit 108 further enhances SNR by identifying,selecting, and tracking the QP state information associated with thedesired signal. The QP state vector provides instantaneous amplitudeinformation over both frequency and time (e.g., a time-frequencyanalysis) that can be analyzed for patterns with which to identify anddistinguish desired signals and artifacts. The current state of theassociated QP amplitudes, taken across the frequency bands, forms a QPstate spectrum whose peaks are associated with time-local quasi-periodiccomponents. Such component peaks associated with a desired signalinclude the pulse rate and its harmonics (the pulse harmonic train),while other peaks may be associated with other quasi-periodic artifactssuch as those arising from respiration (at the breath rate) and/or fromrepetitive motion.

The detection and tracking unit 108 analyzes the pattern of peaks in theQP state spectrum to identify only those peaks associated with thedesired signal by taking advantage of the desired signal's typicallyharmonic pattern and the fact that other quasi-periodic artifacts tendto have harmonics of either low or unstable amplitude above thefundamental. Transients in the input signal appear over multiple bandssimultaneously and/or tend to be relatively short in time, and so areeither rejected outright or are suppressed through filtering in thetracking process.

After peak detection, the detection and tracking unit 108 ranks thepeaks by size for forming measures upon them. To save computation time,only the first three peaks may be kept for harmonic-train analysis, asany peaks beyond this would be either from harmonics (and so alreadypart of a harmonic train) or from lower-level artifacts. For similarreasons, only those peaks of these highest-ranked three with amplitudeat least a minimum fraction of the first (largest) peak may be qualifiedfor further analysis. This fraction may be set to 0.35. Under normal,high-SNR conditions, only one peak may qualify, so this peak may be useddirectly as the detected period.

Otherwise, for each remaining qualified peak, the QP amplitude spectrumis sampled at bands corresponding to that peak and to the harmonicfrequencies of that peak. This train of QP amplitudes is then summedtogether, thus forming the harmonic-train amplitude measure (HTA), onefor each qualified peak. As discussed above, three harmonics may be usedin the computation. Where band indices for harmonics fall above thebands provided, as may happen for higher-frequency fundamental peaks,the amplitude contributions of those harmonics are set to zero. TheseHTAs are again ranked according to the measure, with only the largesttwo kept.

The detection and tracking unit 108 then applies a set of Peak-PairAmplitude-Measure rules to this pair of HTA to select the one mostassociated with the desired signal. In one implementation, the set ofrules is as follows:

-   -   If larger peak has higher frequency band        -   Accept this peak (smaller, lower-rate peak is likely an            artifact)    -   Otherwise (lower-rate peak is largest: check absolute, relative        rate rules)        -   If largest peak has band with rate above Upper Breath Rate            Band Limit            -   Accept this peak (rate is too high to be breath                artifact)        -   Otherwise, check band centers of peak pair for harmonic            relationship            -   Compute peak centroids (interpolated peaks)            -   Compare interpolated band centroids for harmonic pattern                -   Frequency ratio in region of 2/1 or 3/1                -   Ratio within 1.2 bands @ 9 bands per octave            -   If band relationship is harmonic Compare pair of                harmonic train measure amplitudes                -   If smaller peak is <0.9*larger peak                -    Accept larger peak (smaller peak is likely 2nd                    harmonic)                -   Otherwise                -    Accept smaller peak (larger peak likely artifact at                    PR/2)            -   Otherwise (band relationship is not harmonic)                -   If smaller peak is >0.62*larger peak                -    Accept smaller peak (peak large enough to be pulse)                -   Otherwise                -    Accept larger peak (smaller peak likely artifact)                    In the above rules, the Upper Breath Rate Band Limit                    may correspond to band 12 at 65 BPM (a rate                    corresponding to an upper limit of                    hyperventilation). In the case that only one peak                    occurs in the spectrum, this peak is accepted by                    default. In the case that no peaks occur in the                    spectrum, the largest-amplitude band in the spectrum                    may be accepted as the peak.

To compute the peak centroids in the above rules, the detection andtracking unit 108 computes an interpolated band-index using a centroid(center-of-mass) computation. The central band is computed as theband-by-band sum of the product of band indices and corresponding bandamplitudes divided by the band-by-band sum of the band amplitudes. Thebands used in the computation include the peak band and the straddlingbands. If the straddling bands satisfy a symmetry criterion, then theyare both used along with the peak band. If not, then the largest of thestraddling bands is used along with the peak band.

The symmetry criterion is defined as the absolute difference in theamplitudes of the straddling bands divided by the mean of thoseamplitudes (i.e., an amplitude-similarity measure equivalent to aBland-Altman measure). This measure is compared to a threshold, whichmay be set to a value of 0.1 (i.e., a similarity of +/−10%), andsimilarity is declared if the measure is no more than this thresholdvalue.

The detection and tracking unit 108 uses the accepted HTA to select theband upon which it is based, which is in turn associated with thefundamental of the pulse period. The detection and tracking unit 108then uses the QP state information associated with this band and itsharmonic bands to update a QP harmonic state vector dedicated totracking the QP information for these n_(H) harmonics. Harmonics withband indices past the upper limit are flagged as such for appropriatetreatment in the QP-domain harmonic state vector filtering and amplitudestatistics processing unit 110.

Updates to the QP harmonic state information are indicated by an advancein the QP time index accompanied by a change in the phase label(indicating a phase advance) in any of the contained harmonics. Theseupdates are detected for each harmonic of each of the I and R channels,at which points corresponding amplitude and interval statistics areupdated. For forming stable amplitude statistics, the QP amplitudes areindividually smoothed through scaled integral kernels operating in QPspace. The integral scales are set to a number of QP's that naturallymatch the underlying wavelet scale support for the fundamental cardiaccycle. For the system 100, this translates to 2.5 cardiac cycles, or 10QP's at 4 QP's per cycle for the fundamental period. For the harmonics,the scale is increased proportionally with the harmonic number. Thiscauses the harmonics to all closely follow the same time support, sothat the smoothing is effectively synchronized over the same time scalefor all harmonics. At QP updates where the harmonic is flagged as beingout of bank range, the input to the amplitude smoothing is set to zero.Second-order scaled integral kernels may provide sufficient smoothing atthis stage.

The tracked band corresponding to the lowest harmonic has frequencyinformation corresponding to the fundamental period of the PPG pulsewave and thus the pulse interval (PI). The detection and tracking unit108 of the I channel determines the pulse interval estimate by computingthe instantaneous period of the fundamental harmonic, referred to as theQP “atomic period.” Extracting this atomic period provides the pulseinterval PI_(QP) at each QP update. As described in U.S. Pat. No.7,706,992, this is based upon computing time and phase differences fromone QP update to the next of this particular component, which in turnyields information about the period of the underlying frequencycomponent with high precision. The QP atomic period in this case isexpressed as follows:PI _(QP) =dT _(QP) /dP _(QP)The value PI_(QP) corresponds to the (instantaneous) atomic pulseinterval in units of seconds-per-cycle. The value dT_(QP) corresponds tothe time interval from the previous QP update to the current one, inunits of seconds, for the fundamental harmonic. The value dP_(QP)corresponds to the advance in phase value associated with the number ofquarter-phase labels that were traversed from the previous QP Stateupdate to the current one, and can be evaluated according to thefollowing table:

TABLE 6 Number of QP phase Description of QP Phase change dP_(QP) labelstraversed State Transition (in units of cycles) 0 No change 0 1 Basicphase advance 0.25 2 Phase advance with skip 0.5 −1 Phase reversal −0.25

The number of QP phase labels traversed corresponds to the incrementencountered along the cyclic sequence . . . A→B→C→D→A . . . in thecurrent QP state transition, as described in U.S. Pat. No. 9,530,425.The basic phase advance (e.g., an increment of one) is the most commonphase change. A phase change of zero is possible if the current QPupdate is associated with a change in selected fundamental band thathappens to update to the same phase label as that of the selectedfundamental band at the preceding QP. A phase label change of 2 issimilarly possible with a change in selected fundamental band, but withan inversion of phase from the previous band to the next. Phase reversalis typically associated with transients in the signal, or with bandslocated far from any significant periodic components and thus exhibitinglow relative band amplitude. The system 100 can be designed to mitigateboth of these undesirable conditions, thus making phase reversal ahighly unlikely transition type to be encountered.

The detection and tracking unit 108 may then update the atomic pulseinterval PI_(QP) at each QP update of the fundamental harmonic accordingto the following conditions and rules:

-   -   If PI_(QP) is an invalid floating-point number (Inf, NaN) or is        negative        -   Force PI_(QP) to the period corresponding to the center            frequency of the selected fundamental wavelet band    -   Otherwise, if PI_(QP) corresponds to a center frequency at least        one wavelet band above or below the selected fundamental wavelet        band        -   Force PI_(QP) to the period corresponding to the center            frequency of the wavelet band above or below the selected            fundamental wavelet band, respectively    -   Otherwise, PI_(QP) is qualified as computed above        A tracking update can be defined to occur upon a QP update of        the fundamental harmonic.

As shown in FIG. 1 , the detection and tracking unit 108 of the Ichannel provides PI_(QP) and associated band index information to thedetection and tracking unit 108 of the R channel. The fundamentalpulse-related periodicity information underlying the pulse modulation isessentially common to both PPG signals, as the timing of the modulationsis primarily related to the cardiac cycle and the mechanics ofperfusion. Hence, the identification of the bands for the harmonicpattern may be computed on one channel (the “master”), and theassociated band-selection information then passed to the other channel(the “slave”) and used for harmonic-tracking purposes. In the system100, the I channel is the master, as it typically exhibits higher SNR.The bands of the QP state vector that are associated with the desiredpulse harmonic train thus contain concentrated, high SNR time-varyingamplitude and phase/frequency information sufficient to represent(and/or reconstruct) each harmonic of the pulse modulation component ofthe PPG signal, and by extension, the modulation component of the PPGsignal itself.

The process of periodicity detection and period-band selection occurs ateach QP state update of the I channel, as does the updating of its QPharmonic state vector. The QP harmonic state vector for the R channel isupdated at each QP state update of the R channel, and uses the currentperiod-band that is selected at that update time. The detection andtracking unit 108 provides the two QP harmonic state vectors to theQP-domain harmonic state vector filtering and amplitude statisticsprocessing unit 110.

The system 100 includes a pulse interval (PI) filter 112 that performsQP-synchronized filtering to generate an instantaneous pulse intervalPI_(inst). To maintain time alignment in the reported pulse rate, thepulse interval PI_(QP) is processed with the same QP time-basedfiltering/smoothing operation, e.g., smoothed through a scaled integralkernel with the same parameters as that applied to the fundamentalharmonic QP amplitude, as used in the QP-domain harmonic state vectorfiltering and amplitude statistics processing unit 110, updated uponeach QP update of the first harmonic to produce the instantaneous pulseinterval PI_(inst) synchronized to the smoothed harmonic amplitudes.

The QP-domain harmonic state vector filtering and amplitude statisticsprocessing unit 110 performs individual per-harmonic filtering ofparameters (QP-space linear smoothing) to form robust moving measures ofthe harmonic amplitude, QP-synchronized filtering of the DC component ofthe PPG signal, combination of the harmonic amplitude to form robustmeasure of the AC component pulse amplitude, and combination of the ACcomponent pulse amplitude with the DC information to form robust movingmeasures (A_(NI) for the I signal path, A_(NR) for the R signal path) ofthe normalized percent-fraction PPG pulse modulation. The QP-domainharmonic state vector filtering and amplitude statistics processing unit110 filters individually the QP amplitude parameters for each harmonicto provide time-based smoothing of statistics for general suppression ofvariability and noise at the output of the tracking process. Theseparated DC components, I_(dc) and R_(dc), of the PPG signals aresmoothed through parallel scaled integral kernels, with the sameparameters as that applied to the fundamental harmonic QP amplitude, andupdated at the same QP updates. This produces smoothed DC componentsI_(dcs) and R_(dcs) synchronized to the smoothed harmonic amplitudes.

By filtering each harmonic individually, the amplitude associated witheach fine aspect of the morphology can be smoothed independently, addingindependent robustness against interference that may occur in bandsneighboring a particular harmonic. Additionally, as these harmonic statefilters are updated independently upon each individual harmonic's QPupdate, computational savings may be realized relative to per-samplefiltering, especially on the lower harmonics as the QP update ratesscale proportionally to frequency. In order to maintain time alignmentbetween the AC and DC components, the unit 110 may also sample the DCcomponents of the PPG signal at each QP update of the first harmonic(the fundamental), and process them using the same QP time-basedfiltering/smoothing operation.

The unit 110 combines the smoothed harmonic amplitudes to form robustmoving estimates of the amplitude of the AC component, i.e., the pulseamplitude of the PPG signal. At each tracking update, the smoothedstatistics can be combined as follows. The smoothed harmonic amplitudesfor each PPG channel are linearly combined to form moving estimatesa_(IR) and a_(R) of the AC components corresponding to the PPG pulseamplitudes. The linear combination weights applied to the smoothedharmonic amplitudes need not be uniform. Reducing the weight on thefirst harmonic works to suppress the effect of artifacts, which tend toinfluence the lowest harmonic the most at low SNRs, withoutsignificantly impacting performance at higher SNRs. Weights for thelinear combination may be as follows, in order of harmonic number:0.125, 1.0, 1.0.

The moving estimates a_(IR) and a_(R) are then normalized by thecorresponding smoothed DC component value, resulting in robust movingmeasures of the normalized percent-fraction PPG pulse modulation, shownas A_(NI) and A_(NR) in FIG. 1 . The moving measures of the normalizedpercent-fraction PPG pulse modulations A_(NI) and A_(NR) are computed ateach tracking update as follows:A _(NI) =a _(R) /I _(dcs)A _(NR) =a _(R) /R _(dcs)

The system 100 includes a division unit 114 that provides the movingmeasure R_(N) of the normalized modulation ratio of A_(NI) and A_(NR).The normalized modulation ratio R_(N)=A_(NR)/A_(NI), which forms theinstantaneous moving measure from which SpO₂ is mapped.

The system 100 includes statistical filters 116 a and 116 b. Statisticalfilter 116 a forms smoothed moving estimates R_(PPG) of the normalizedmodulation ratio R_(N). Statistical filter 116 b forms smoothed movingestimates PI_(PPG) of instantaneous pulse interval PI_(inst). Eachstatistical filter performs statistically-based smoothing, based upon amoving stack filter, to provide a final smoothing function and to removeany remaining large, outlying transient excursions from the finaloutput. Identical smoothing filters are applied to both R_(N) andPI_(inst) in parallel to generally preserve time alignment, resulting infinal moving PPG measures R_(PPG) and PI_(PPG).

The statistical filters 116 a and 116 b are designed to remove anyremaining large, outlying transient excursions from the final outputsand to generally stabilize the output readings. An averaging timeT_(stat)=2.5 seconds may produce an acceptable level of smoothing whilekeeping time lag as low as possible. Hence, at each tracking update, theaveraging for each statistical filter 116 a and 116 b is updated usingthe respective values of R_(N) and PI_(inst) occurring within theinterval T_(stat) seconds preceding (and including) the current trackingupdate. Rather than performing simple moving averages, smoothed mediansare computed by taking the averages of the central values, in apercentile sense, over a range around the pure medians. Thecentral-range average may be set to include those values in theinter-quartile range centered around the median value, to preserverobustness against outliers while also producing well-smoothed outputsover time. By using the same parameters and update times,synchronization may be preserved between the two smoothed outputstatistics.

The system 100 includes mapping units 118 a and 118 b. Mapping unit 118a performs mapping of R_(PPG) to output SpO₂ values. Mapping unit 118 bperforms mapping of PI_(PPG) to output pulse rate PR values.

The normalized modulation ratio R_(PPG) can be mapped to SpO₂ through apolynomial function, for example a second-order polynomial. Thepolynomial coefficients may be determined through a calibrationprocedure, e.g., by collecting SaO₂ and corresponding R_(PPG) values ata hypoxia lab from subjects following a standard ramp-down protocol,then performing a second-order fit to form the map to SpO₂. Variousprobes may be supported, for example finger probes and ear probes, bycollecting R_(PPG) values using each probe and then forming separatecorresponding maps for each.

The polynomial is evaluated as follows:

$y = {\sum\limits_{n = 0}^{2}{a_{n}x^{n}}}$where R_(PPG) is the input value x, and output y is the mapped value ofSpO₂. In one implementation, the values an are as follows for finger andear probes:

TABLE 7 N a_(n) (Finger Probe) a_(n) (Ear Probe) 0 112.02185 115.49644 1−21.632549 −27.554501 2 −4.2294321 −3.3103607To prevent excess excursion in output values, the SpO₂ output valuerange may be limited to an upper value of 100% and a lower value of 0%.

The instantaneous Pulse Interval PI_(PPG) is typically mapped to thepulse rate PR through the following formula:PR=60·F _(s) /PI _(PPG)where the Pulse Interval is expressed in samples and PR in BPM. Toprevent excess excursion in output values, the PR output value range maybe limited to an upper value of 240 BPM and a lower value of 30 BPM.

Local losses in signal amplitude may occur due to complete signal loss,such as due to lead detachment, or from lack of pulse modulation, suchas due to extremely low perfusion or due to cardiac pause or asystole.Because SpO₂ and PR are not well-defined under these conditions, thesystem 100 includes an output qualification unit 122 that uses theamplitude level of the normalized modulation signal to qualify the pulsemodulation amplitude for sufficient level to support confidence indesired accuracy. This amplitude level may be based upon forming amoving estimate I_(ama) of the amplitude of AC component I_(ac) alongwith a time-aligned moving estimate I_(md) from DC component I_(dc) andcomparing the ratio of these values to a threshold, placed at the pointwhere performance is observed experimentally to drop significantly.

The moving AC amplitude estimate I_(ama) and DC component estimateI_(md) are formed by smoothing the absolute value of AC component I_(ac)and DC component I_(dc) respectively with moving average filters. Thefilters may be implemented with scaled integral cascades having the sameparameters, thus having linear phase with time alignment between theestimates. The smoothing may be set as second order with a time scale of3 seconds, to sufficiently smooth pulsatile components from theestimates while still maintaining time-responsiveness to drops and risesin amplitude. For the processing sample rate of F_(s)=225 Hz, thefollowing scaled-integral parameters may be used for the smoothingfilters:

TABLE 8 w_(sm) N_(i) D_(sm) 675 2 674

A flag f_(sig), which is asserted for normalized modulation signalamplitude levels generally above a confidence threshold, and de-assertedbelow, is used for qualifying the signal level for pulse oximetry. Incases where the signal level moves through the threshold value slowlywith small ripples superimposed, chattering of the qualification flagcould result. This can be mitigated by adding hysteresis to thethreshold (e.g., inversely relating the threshold to the qualificationstate). Also, due to the time lag inherent in the various processingstages, time delay may be added to the assertion and de-assertion of theflag so that the indication of signal quality aligns in time with theoutput readings of the pulse oximetry.

The output qualification unit 122 can meet both of these objectives byimplementing the signal-qualification process with a state machine. Thestate definitions and state-transition rules are as follows:

Initialization: State=NOT_QUAL//f_(sig)=False

TABLE 9 Current State Condition Next State//Action NOT_QUALI_(ama)/I_(md) > threshU WAIT_UP//Reset Wait_Count WAIT_UPI_(ama)/I_(md) < threshD NOT_QUAL (Else) (Else) Wait_Count > TdelayUQUAL//f_(sig) = True QUAL I_(ama)/I_(md) < threshD WAIT_DN//ResetWait_Count WAIT_DN I_(ama)/I_(md) > threshU QUAL (Else) (Else)Wait_Count > TdelayD NOT_QUAL//f_(sig) = FalseThe threshold values may be set as follows:

TABLE 10 Thresholds (Upward, Downward) Values threshU 0.00028 threshD0.00014The delay times may be set as follows:

TABLE 11 Delays (Upward, Values Downward) (seconds) TdelayU 22.5 TdelayD5

The output qualification unit 122 provides the signal qualification flagf_(sig) to an output interface 124 as a means of suppressing display ofspurious or erroneous pulse oximetry output during unfavorable signalconditions. Delays may be chosen to cause de-assertion of flag f_(sig)before deterioration of signal conditions and re-assertion of flagf_(sig) after restoration of signal conditions, seeking to ensurefavorable signal conditions only when flag f_(sig) is asserted.

The system 100 can continuously display average SpO₂ values and averagePR values on a display 120. The display 120 can be a display of abedside monitor, a central server station, or a mobile device asdescribed in U.S. Publication No. 2015/0302539. The display 120 canalert a user of the display 120 if an alarm state for the patient isdetected. The display 120 can alert the user by, for example, flashing,changing color, becoming enlarged, or otherwise visually indicating analarm state. For example, if the patient's SpO₂ level increases to abovean upper acceptable bound or decreases to a lower acceptable bound, thedisplay 120 can flash to indicate an alarm state to a user of thedisplay 120. The system 100 can alert the user by triggering an audiblealarm. In some implementations, different audible alarms can be used toindicate different alarm states or severity of an alarm state. Thevolume of an audible alarm can also be used to indicate the relativeseverity of an alarm state.

The system 100 can also alert users to an alarm state by causing one ormore portions of the display 120 to flash, issuing an audible alarm, ortransmitting alerts to other devices. For example, a bedside monitor cantransmit an alert to a central server which can then identify one ormore caregivers to whom to direct the alert associated with a patient.The central server can then transmit alerts to devices associated withthe identified caregivers.

The pulse oximetry system 100 can be useful in any setting where apatient's oxygenation is unstable, including intensive care, operating,recovery, emergency and hospital ward settings, pilots in unpressurizedaircraft, for assessment of any patient's oxygenation, and determiningthe effectiveness of or need for supplemental oxygen. The system 100 canprovide continuous and immediate oxygen saturation values, which are ofcritical importance in emergency medicine and are also very useful forpatients with respiratory or cardiac problems, especially chronicobstructive pulmonary disease (COPD), or for diagnosis of some sleepdisorders such as apnea and hypopnea.

In some implementations, the system 100 can be implemented as a portablebattery-operated pulse oximeter. Such portable battery-operated pulseoximeters are useful for pilots operating in a non-pressurized aircraftabove 10,000 feet (12,500 feet in the U.S.) where supplemental oxygen isrequired. Portable pulse oximeters are also useful for mountain climbersand athletes whose oxygen levels may decrease at high altitudes or withexercise. Portable pulse oximeters can employ the system 100 to monitora patient's blood oxygen and pulse, serving as a reminder to check bloodoxygen levels.

FIG. 4 shows a system 400 that includes a patient worn sensor 402 incommunication with a display device 404, such as a display of a bedsidemonitor, a central server station, or a mobile device as described inU.S. Publication No. 2015/0302539. The display device 404 displaysinformation for a patient. The display device 404 can display a userinterface 406 that includes information received from the patient wornsensor 402 and/or information associated with a patient associated withthe patient worn sensor 402. The patient worn sensor 402 can be, forexample, a chest sensor as described in U.S. Publication No.2015/0302539. The patient worn sensor 402 can include contacts forattaching to the skin of a patient and recording various patient vitalsigns such as blood pressure, body temperature, respiratory rate, bodyimpedance, blood oxygenation, heart rhythm (via ECG), and heart rate.

The patient worn sensor 402 can wirelessly communicate with the displaydevice 404 through a wireless connection 408 using a wirelesscommunication protocol such as, for example, Bluetooth, WiFi, or acellular protocol. The patient worn sensor 402 can transmit vital signinformation for the patient to the display device 404 through thewireless connection 408. In some implementations, the patient wornsensor 402 can perform processing on the collected vital signinformation prior to transmission of the information to the displaydevice 404, while in some implementations, the patient worn sensor 402can transmit raw vital sign information to the display device 404instead of or in addition to processed information. The display device404 can be a touch screen device, such as a tablet, that is capable ofreceiving touch screen inputs. In some implementations, the displaydevice 404 can receive input from a keyboard, mouse, input buttons, orone or more devices capable of recognizing voice commands. In someimplementations, the display device 404 can be controlled using a devicein wireless communication with the display device 404, such as a mobilephone. In some implementations, the display device 404 is a “headless”device that does not include direct user input and/or outputfunctionality, but rather merely serves as a processing device forprocessing raw vital sign information received from the patient wornsensor 402, detecting alarm states, transmitting alerts to other devicesin communication with the display device 404, and transmitting patientinformation to one or more central servers. In such cases, the displaydevice 404 would not include a display.

The user interface 406 displays information for a patient associatedwith the patient worn sensor 402. For example, electrodes of the patientworn sensor 402 can be in contact with the patient's skin and collectvital sign information which is transferred to the display device 404through the wireless connection 408, processed by the display device404, and displayed as part of the user interface 406. The user interface406 shows various vital sign waves and numeric levels.

For example, the user interface 406 shows a heart rate waveform 410 forthe patient as well as a numeric heart rate or pulse rate value 412 forthe patient. In the example shown, the pulse rate value 412 for thepatient is 80 beats per minute. The user interface 406 indicates at 414an acceptable heart rate level for the patient as falling between 50 and120 beats per minute. Being as the current heart rate for the patient of80 beats per minute falls within the indicated acceptable range, thereis not currently an alarm state for heart rate for the patient. This isindicated by an icon 416 of a bell superimposed with an “X” symbol. Theicon 416 indicates that the current heart rate of the patient is withinthe acceptable range. In a situation in which the heart rate for thepatient is not within the acceptable level, the icon 416 can change toindicate an alarm state. For example, the “X” can disappear from theicon 416 and the icon 416 can light up or flash to indicate an alarmstate. Additionally, the display device 404 can emit an audible alarm toalert nearby caregivers to an alarm state for the patient. In someimplementations, other portions of the user interface 406 can flash orotherwise indicate an alarm state. For example, the displayed pulse ratevalue 412 can flash when the patient's pulse rate is outside of anacceptable level. In some implementations, the icon 416 (or otherportions of the user interface 406) can flash at varying rates toindicate the severity of a particular alarm state. For example, the icon416 can flash faster the further the patient's heart rate is from theacceptable range.

The user interface 406 shows a blood oxygenation waveform 418 and anumeric blood oxygenation value 420 for the patient. The user interface406 also shows, at 422, an acceptable blood oxygenation range for thepatient. The user interface 406 further includes an icon 424 indicatingthat the blood oxygenation level for the patient is within theacceptable range (indicated by an “X” symbol superimposed over a bell,indicating that the alarm is “off”).

FIG. 5 is a flowchart showing examples of operations 500 performed by apulse oximetry system. The operations 500 may be performed by a systemof one or more computers, such as the system of FIG. 1 . The operations500 may include details that have been discussed above.

At 502, the system receives signals corresponding to wavelengths oflight detected by an optical sensor placed in proximity to a patient'sbody. The signals include a first signal corresponding to a firstwavelength of light detected by the optical sensor and a second signalcorresponding to a second wavelength of light detected by the opticalsensor.

Operations 504 through 514 are performed for each received signal of thefirst signal and the second signal. At 504, the signal is optionallyupsampled to increase the sample rate. At 506, the signal is separatedinto an AC signal and a DC signal.

At 508, the AC signal is separated into component signals, where each ofthe component signals represents a frequency-limited band. Separatingthe AC signal into the component signals may include filtering the ACsignal using a bank of bandpass filters having adjacently-spaced bandcenters and normalizing outputs of the bank of bandpass filters togenerate the component signals.

At 510, the system analyzes the component signals through a fractionalphase transformation to identify a desired component signal and harmonicsignals associated with the desired component signal. To identify thedesired component signal and the harmonic signals, the system maydetermine an atomic period by computing an instantaneous period of alowest harmonic signal derived from the first signal. For the firstsignal, the desired component signal and the harmonic signals may beidentified based on the atomic period and by analyzing time-alignedamplitude values of the component signals and the relationships ofamplitudes of the harmonic signals. Based on the atomic period, thesystem may determine an atomic pulse interval, and the desired componentsignal and the harmonic signals for the second signal may be identifiedbased on the atomic pulse interval.

At 512, the system smooths the desired component signal, the harmonicsignals, and the DC signal. At 514, the smoothed desired componentsignal, the smoothed harmonic signals, and the smoothed DC signal arecombined to generate a modulation signal. Combining the smoothed desiredcomponent signal, the smoothed harmonic signals, and the smoothed DCsignal may include linearly combining the smoothed desired componentsignal and the smoothed harmonic signals to generate a combined signal,and normalizing the combined signaled using the smoothed DC signal togenerate the modulation signal.

At 516, the system generates a modulation ratio signal based on themodulation signal derived from the first signal and the modulationsignal derived from the second signal. At 518, the peripheral oxygensaturation (SpO₂) of the patient's body is determined based on themodulation ratio signal. Determining the SpO₂ of the patient's body mayinclude generating smoothed moving estimates of the modulation ratiosignal and mapping the smoothed moving estimates to output valuesrepresenting the SpO₂ of the patient's body. The system may alsodetermine a pulse rate of the patient's body based on the atomic pulseinterval determined above.

FIG. 6 is a block diagram showing an example of a computing system 600executing a computer program that implements the above-describedoperations, data structures, and/or programs contained on a computerreadable medium 602, and executed by a central processing unit (CPU)604. The CPU 604 may be an x86 series CPU or other CPU known in the art.Input to execute the program can, in some implementations, come by wayof a keyboard 606. In some implementations, input to execute the programcan come by way of a peripheral device such as a mouse, light pen, touchscreen, touch pad, or any other input device known in the art. In someimplementations, the described operations can be called by anotherprogram to execute and process oximetry data. Once processed, dataprocessed using the above-described operations, data structures, and/orprograms can be outputted to a display 608, or sent via an internet 610or other wireless communications channel to another user terminal 612.In some implementations, the output can be placed into a database 614 oranother database located off-site.

FIG. 7 is a block diagram showing an example of a computing system thatdepicts a host 700 which may, in some implementations, include a set ofunits or modules such as a communications module 702 and associatedcommunications link 704 for communicating with a patient worn sensor, amicroprocessor unit 706 (MPU) including a computer readable medium 708containing a set of application instructions, an associated storagemodule 710 for storing patient data, a Human-Computer Interface 712 bywhich an operator (e.g., a physician, technician, or patient) mayinteract with the MPU 706 and which may include a visual display,auditory display, keyboard, mouse, touch screen, haptic device, or othermeans of interaction. The host 700 also includes an input/output module714 for generally connecting to, and communicating with, computerperipherals such as for example printers, scanners, and/or dataacquisition or imaging equipment, and a network communications module716 for communicating with other hosts on a computer network such asEthernet and/or a wireless network, or WiFi.

The features described in this disclosure can be implemented in digitalelectronic circuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor, and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing context.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer area processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application specific integrated circuits). To provide forinteraction with a user, the features can be implemented on a computerhaving a display device such as a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor for displaying information to the user and akeyboard and a pointing device such as a mouse or a trackball by whichthe user can provide input to the computer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet. The computer system caninclude clients and servers. A client and server are generally remotefrom each other and typically interact through a network, such as thedescribed one. The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as Such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software or hardwareproduct or packaged into multiple software or hardware products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A non-transitory computer readable storage mediumencoded with a computer program, the program comprising instructionsthat when executed by one or more data processing apparatus cause theone or more data processing apparatus to perform operations comprising:receiving a first signal corresponding to a first wavelength of lightdetected by an optical sensor placed in proximity to a patient's body;receiving a second signal corresponding to a second wavelength of lightdetected by the optical sensor; for each received signal of the firstsignal and the second signal: separating the signal into an AC signaland a DC signal; separating the AC signal into component signals,wherein each of the component signals represents a frequency-limitedband, analyzing the component signals through a fractional phasetransformation to identify a desired component signal and harmonicsignals associated with the desired component signal, smoothing thedesired component signal, the harmonic signals, and the DC signal, andcombining the smoothed desired component signal, the smoothed harmonicsignals, and the smoothed DC signal to generate a modulation signal;generating a modulation ratio signal based on the modulation signalderived from the first signal and the modulation signal derived from thesecond signal; and determining peripheral oxygen saturation (SpO₂) ofthe patient's body based on the modulation ratio signal.
 2. Thenon-transitory computer-readable storage medium of claim 1, wherein: theoperations further comprise, for each received signal of the firstsignal and the second signal, converting the received signal having afirst sample rate to an upsampled signal having a second sample rate,the second sample rate being higher than the first sample rate; andseparating the signal into the AC signal and the DC signal comprisesseparating the upsampled signal into the AC signal and the DC signal. 3.The non-transitory computer readable storage medium of claim 1, whereinfor the first signal, identifying the desired component signal and theharmonic signals comprises analyzing time-aligned amplitude values ofthe component signals.
 4. The non-transitory computer readable storagemedium of claim 1, wherein for the first signal, identifying the desiredcomponent signal and the harmonic signals comprises analyzingrelationships of amplitudes of the harmonic signals.
 5. Thenon-transitory computer readable storage medium of claim 1, wherein: theoperations further comprise determining an atomic period by computing aninstantaneous period of the desired component signal.
 6. Thenon-transitory computer readable medium of claim 5, wherein for thefirst signal, identifying the desired component signal and the harmonicsignals comprises identifying the harmonic signals based the atomicperiod.
 7. The non-transitory computer readable storage medium of claim5, wherein: the operations further comprise determining an atomic pulseinterval based on the atomic period.
 8. The non-transitory computerreadable storage medium of claim 7, wherein for the second signal,identifying the desired component signal and the harmonic signals isbased on the atomic pulse interval.
 9. The non-transitory computerreadable storage medium of claim 7, wherein: the operations furthercomprise determining a pulse rate of the patient's body based on thedetermined atomic pulse interval.
 10. The non-transitory computerreadable storage medium of claim 1, wherein separating the AC signalinto the component signals comprises: filtering the AC signal using abank of bandpass filters having adjacently-spaced band centers; andnormalizing outputs of the bank of bandpass filters to generate thecomponent signals.
 11. The non-transitory computer readable storagemedium of claim 1, wherein combining the smoothed desired componentsignal, the smoothed harmonic signals, and the smoothed DC signal togenerate a modulation signal comprises: linearly combining the smootheddesired component signal and the smoothed harmonic signals to generate acombined signal; and normalizing the combined signaled using thesmoothed DC signal to generate the modulation signal.
 12. Thenon-transitory computer readable storage medium of claim 1, whereindetermining the SpO₂ of the patient's body based on the modulation ratiosignal comprises: generating smoothed moving estimates of the modulationratio signal; and mapping the smoothed moving estimates to output valuesrepresenting the SpO₂ of the patient's body.
 13. A computer-implementedmethod comprising: receiving a first signal corresponding to a firstwavelength of light detected by an optical sensor placed in proximity toa patient's body; receiving a second signal corresponding to a secondwavelength of light detected by the optical sensor; for each receivedsignal of the first signal and the second signal: separating the signalinto an AC signal and a DC signal; separating the AC signal intocomponent signals, wherein each of the component signals represents afrequency-limited band, analyzing the component signals through afractional phase transformation to identify a desired component signaland harmonic signals associated with the desired component signal,smoothing the desired component signal, the harmonic signals, and the DCsignal, and combining the smoothed desired component signal, thesmoothed harmonic signals, and the smoothed DC signal to generate amodulation signal; generating a modulation ratio signal based on themodulation signal derived from the first signal and the modulationsignal derived from the second signal; and determining peripheral oxygensaturation (SpO₂) of the patient's body based on the modulation ratiosignal.
 14. The computer-implemented method of claim 13, furthercomprising: for each received signal of the first signal and the secondsignal, converting the received signal having a first sample rate to anupsampled signal having a second sample rate, the second sample ratebeing higher than the first sample rate; and wherein separating thesignal into the AC signal and the DC signal comprises separating theupsampled signal into the AC signal and the DC signal.
 15. Thecomputer-implemented method of claim 13, wherein for the first signal,identifying the desired component signal and the harmonic signalscomprises analyzing time-aligned amplitude values of the componentsignals.
 16. The computer-implemented method of claim 13, wherein forthe first signal, identifying the desired component signal and theharmonic signals comprises analyzing relationships of amplitudes of theharmonic signals.
 17. The computer-implemented method of claim 13,further comprising: determining an atomic period by computing aninstantaneous period of the desired component signal.
 18. Thecomputer-implemented method of claim 17, wherein for the first signal,identifying the desired component signal and the harmonic signalscomprises identifying the harmonic signals based the atomic period. 19.The computer-implemented method of claim 17, further comprising:determining an atomic pulse interval based on the atomic period.
 20. Thecomputer-implemented method of claim 19, wherein for the second signal,identifying the desired component signal and the harmonic signals isbased on the atomic pulse interval.
 21. The computer-implemented methodof claim 19, further comprising: determining a pulse rate of thepatient's body based on the determined atomic pulse interval.
 22. Thecomputer-implemented method of claim 13, wherein separating the ACsignal into the component signals comprises: filtering the AC signalusing a bank of bandpass filters having adjacently-spaced band centers;and normalizing outputs of the bank of bandpass filters to generate thecomponent signals.
 23. The computer-implemented method of claim 13,wherein combining the smoothed desired component signal, the smoothedharmonic signals, and the smoothed DC signal to generate a modulationsignal comprises: linearly combining the smoothed desired componentsignal and the smoothed harmonic signals to generate a combined signal;and normalizing the combined signaled using the smoothed DC signal togenerate the modulation signal.
 24. The computer-implemented method ofclaim 13, wherein determining the SpO₂ of the patient's body based onthe modulation ratio signal comprises: generating smoothed movingestimates of the modulation ratio signal; and mapping the smoothedmoving estimates to output values representing the SpO₂ of the patient'sbody.
 25. A system comprising: an optical sensor configured to light; adisplay apparatus; one or more data processing apparatus coupled withthe optical sensor and the display apparatus; and a non-transitorycomputer readable storage medium encoded with a computer program, theprogram comprising instructions that when executed by the one or moredata processing apparatus cause the one or more data processingapparatus to perform operations comprising: receiving a first signalcorresponding to a first wavelength of light detected by the opticalsensor placed in proximity to a patient's body; receiving a secondsignal corresponding to a second wavelength of light detected by theoptical sensor; for each received signal of the first signal and thesecond signal: separating the signal into an AC signal and a DC signal;separating the AC signal into component signals, wherein each of thecomponent signals represents a frequency-limited band, analyzing thecomponent signals through a fractional phase transformation to identifya desired component signal and harmonic signals associated with thedesired component signal, smoothing the desired component signal, theharmonic signals, and the DC signal, and combining the smoothed desiredcomponent signal, the smoothed harmonic signals, and the smoothed DCsignal to generate a modulation signal; generating a modulation ratiosignal based on the modulation signal derived from the first signal andthe modulation signal derived from the second signal; determiningperipheral oxygen saturation (SpO₂) of the patient's body based on themodulation ratio signal; and causing the display apparatus to display avalue representing the determined SpO₂ of the patient's body.
 26. Thesystem of claim 25, wherein: the operations further comprise, for eachreceived signal of the first signal and the second signal, convertingthe received signal having a first sample rate to an upsampled signalhaving a second sample rate, the second sample rate being higher thanthe first sample rate; and separating the signal into the AC signal andthe DC signal comprises separating the upsampled signal into the ACsignal and the DC signal.
 27. The system of claim 25, wherein for thefirst signal, identifying the desired component signal and the harmonicsignals comprises analyzing time-aligned amplitude values of thecomponent signals.
 28. The system of claim 25, wherein for the firstsignal, identifying the desired component signal and the harmonicsignals comprises analyzing relationships of amplitudes of the harmonicsignals.
 29. The system of claim 25, wherein: the operations furthercomprise determining an atomic period by computing an instantaneousperiod of the desired component signal.
 30. The system of claim 29,wherein for the first signal, identifying the desired component signaland the harmonic signals comprises identifying the harmonic signalsbased the atomic period.
 31. The system of claim 29, wherein: theoperations further comprise determining an atomic pulse interval basedon the atomic period.
 32. The system of claim 31, wherein for the secondsignal, identifying the desired component signal and the harmonicsignals is based on the atomic pulse interval.
 33. The system of claim31, wherein: the operations further comprise determining a pulse rate ofthe patient's body based on the determined atomic pulse interval. 34.The system of claim 25, wherein separating the AC signal into thecomponent signals comprises: filtering the AC signal using a bank ofbandpass filters having adjacently-spaced band centers; and normalizingoutputs of the bank of bandpass filters to generate the componentsignals.
 35. The system of claim 25, wherein combining the smootheddesired component signal, the smoothed harmonic signals, and thesmoothed DC signal to generate a modulation signal comprises: linearlycombining the smoothed desired component signal and the smoothedharmonic signals to generate a combined signal; and normalizing thecombined signaled using the smoothed DC signal to generate themodulation signal.
 36. The system of claim 25, wherein determining theSpO₂ of the patient's body based on the modulation ratio signalcomprises: generating smoothed moving estimates of the modulation ratiosignal; and mapping the smoothed moving estimates to output valuesrepresenting the SpO₂ of the patient's body.
 37. The non-transitorycomputer-readable storage medium of claim 1, wherein the step ofanalyzing generates fractional-phase state vectors corresponding to thedesired component signal and the harmonic signals, the step of smoothingincludes smoothing the fractional-phase state vectors, and the step ofcombining includes combining the smoothed DC signal and the smoothedfractional-phase state vectors to generate the modulation signal. 38.The non-transitory computer-readable storage medium of claim 37, whereineach of the fractional-phase state vectors is updated at each transitionbetween fractional phases, and each of the smoothed DC signal, thesmoothed fractional-phase state vectors, and the modulation signal isupdated when one of the fractional-phase state vectors is updated. 39.The computer-implemented method of claim 13, wherein the step ofanalyzing generates fractional-phase state vectors corresponding to thedesired component signal and the harmonic signals, the step of smoothingincludes smoothing the fractional-phase state vectors, and the step ofcombining includes combining the smoothed DC signal and the smoothedfractional-phase state vectors to generate the modulation signal. 40.The computer-implemented method of claim 39, wherein each of thefractional-phase state vectors is updated at each transition betweenfractional phases, and each of the smoothed DC signal, the smoothedfractional-phase state vectors, and the modulation signal is updatedwhen one of the fractional-phase state vectors is updated.
 41. Thesystem of claim 25, wherein the step of analyzing generatesfractional-phase state vectors corresponding to the desired componentsignal and the harmonic signals, the step of smoothing includessmoothing the fractional-phase state vectors, and the step of combiningincludes combining the smoothed DC signal and the smoothedfractional-phase state vectors to generate the modulation signal. 42.The non-transitory computer-readable storage medium of claim 41, whereineach of the fractional-phase state vectors is updated at each transitionbetween fractional phases, and each of the smoothed DC signal, thesmoothed fractional-phase state vectors, and the modulation signal isupdated when one of the fractional-phase state vectors is updated.